Multimodal Diabetic Retinopathy Detection

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@shadabkhan92

Done Similar tasks lets discuss it over chat As an experienced and multidisciplinary professional, I have the unique skill set that lends itself perfectly to undertaking this project. Carrying the torch of Machine Learning with proficiency in Python and PyTorch, I've successfully executed similar tasks in the past. For instance, I developed ML models that detected diseases with multimodal data like retinal images supplemented with clinical metadata. Moreover, my approach emphasizes explainability and clarity, seamless for sharing in an academic setting. Handling OLIVES dataset won't be a challenge as I’m adept at tackling complex datasets, including any class imbalance by implementing suitable techniques while efficiently documenting each step of the preprocessing pipeline. My capability to design comprehensive architectures aligns with your requirements. Plus, I emphasize clean programming practices resulting in reproducible code.

@nrajuu07

I'll build a clean, reproducible multimodal ML pipeline on the OLIVES dataset — handling preprocessing, class imbalance, and a PyTorch/TensorFlow model fusing fundus images with clinical metadata. Deliverables include all quantitative metrics (Accuracy, F1, AUC, Cohen's Kappa), a full explainability suite (Grad-CAM heatmaps, attention maps, ROC/PR curves, Dice/IoU), a Jupyter notebook that runs end-to-end, saved weights with inference script, and a short clinical interpretability write-up — all organised, well-commented, and presentation ready.

@sudhir0112

Hi! I'm Sudhir Jain — MIT graduate, ML Engineer with deep expertise in Computer Vision, medical imaging, and multimodal deep learning. I specialize in building AI systems for ophthalmological and diagnostic applications. For your Multimodal Diabetic Retinopathy Detection project, I'll build: fusion of fundus images + clinical metadata (multimodal architecture), CNN-based retinal feature extraction (EfficientNet/ResNet backbone), severity grading (DR stages 0-4), attention mechanisms for lesion localization (Grad-CAM, saliency maps), and cross-modal attention for improved accuracy. Experience with APTOS, Kaggle DR datasets. Rigorous validation with medical-grade metrics (kappa score, AUC). Python, PyTorch/TensorFlow. Let's build this together!

@waleeds05

Hi, I see you need a solid multimodal model for diabetic retinopathy detection using the OLIVES dataset and a clear focus on explainability. I can definitely help with that. I’ll prep the dataset, manage any class imbalances, and document everything as we go. For the model, I'll set it up in PyTorch (or TensorFlow if you prefer) and ensure it’s clean and reproducible. I'll also handle all the metrics and visual outputs you mentioned,like confusion matrices and heat maps,so you have plenty to showcase in your academic presentation. I’ve worked on similar ML projects before, focusing on both performance and interpretability, so I know how to make it comprehensive without losing clarity. Also, I can get you the initial deliverable in about 10 days, so you won’t have to wait long. Let me know if you have any specific preferences for the model architecture or if there's anything else you want to add! best regards Walled Saleem

@bishtchirag13

Hi there, I specialize in medical computer vision and PyTorch. I can build this educational pipeline for the OLIVES dataset, focusing heavily on the rigorous explainability and evaluation metrics required for your academic presentation. Given the project scope, here is my exact approach to deliver clean, presentation-ready code: 1. Multimodal Architecture: I will build a PyTorch pipeline that fuses fundus images (using a lightweight pre-trained CNN like ResNet50 for image feature extraction) with the tabular clinical metadata (via a dense network). I will handle class imbalance using weighted loss functions to ensure highly reliable F1 and Cohen Kappa scores. 2. Explainability & Visuals (Your Core Focus): In medical ML, interpretability is everything. I will implement: Grad-CAM Heatmaps: Overlaid directly on the retinal images to prove the model is looking at actual lesions/biomarkers, not background noise. Performance Plots: ROC, PR curves, Calibration plots, and Confusion Matrices. Note: Since Dice and IoU are typically segmentation metrics, I will apply them specifically to the biomarker masks provided in the OLIVES dataset. 3. Deliverables: You will receive a perfectly documented Jupyter Notebook (runnable end-to-end), saved .pth weights, the mapped folder of visual outputs, and the 2-page clinical interpretability report. I am ready to start right now. Let's get this built! Best regards, Chirag Bisht

@migeluys

I am a perfect fit for your project requiring a clean, professional, and user-friendly multimodal AI/ML model with seamless integration of fundus images and clinical metadata from the OLIVES dataset. Your focus on rigorous evaluation and explainability aligns perfectly with my approach. I offer expertise in Python, PyTorch, and TensorFlow, ensuring automated preprocessing, balanced dataset handling, and reproducible code. While I am new to freelancer, I have tons of experience and have done other projects off site. I would love to chat more about your project! Regards, Migel Uys

@mariumbawany1234

Hi! I’d love to work on your diabetic retinopathy detection project using the OLIVES dataset. I can build a clean and beginner-friendly AI/ML pipeline with proper preprocessing, multimodal model training, evaluation metrics, and explainability outputs like Grad-CAM, ROC curves, confusion matrix, attention maps, Dice score, IoU, and feature importance analysis. The notebook will be fully organised, well-commented, and easy to present in an academic setting. I’ll also provide saved model weights, inference script, visual outputs folder, and a short explanation report covering model performance and clinical interpretability. I have experience with Python, PyTorch/TensorFlow, data preprocessing, visualization, and ML model evaluation, and I’ll make sure the project is reproducible and presentation-ready on a fresh environment. Looking forward to working with you!

@Shubhamy39275

Hi, I can help build a clean and modular deep-learning pipeline for diabetic retinopathy and DME analysis using fundus/OCT image data. I am comfortable working with both PyTorch and TensorFlow/Keras depending on the project requirements. I have experience with image-processing and deep-learning workflows including preprocessing, model training, evaluation metrics, visualization, and notebook-based experimentation. For this project, I can help implement: • Fundus and OCT preprocessing workflow • CNN/Transformer-based multimodal image pipeline • Classification and evaluation workflow • Metrics including Accuracy, Precision, Recall, F1-score, AUC, ROC curves, and confusion matrices • Explainability outputs such as Grad-CAM, attention visualization, and retinal heatmaps • Organized training/inference scripts with reproducible experiment outputs and documentation The implementation will be clean, modular, and suitable for academic experimentation and presentation purposes. Looking forward to discussing the project further.

@chandru200727

I am excited to contribute to your educational AI/ML project on diabetic retinopathy detection using the OLIVES dataset. With strong expertise in Python, PyTorch, and deep learning, I will design a clean, reproducible multimodal pipeline that integrates fundus images with clinical metadata. My approach will carefully address class imbalance and ensure transparent preprocessing documentation. I will deliver rigorous evaluation with metrics including Accuracy, Precision, F1‑score, AUC, and Cohen Kappa on a held‑out test set. To highlight clinical interpretability, I will generate confusion matrices, ROC and PR curves, calibration plots, Dice and IoU scores, alongside feature‑importance analysis. Grad‑CAM and attention visualizations will be overlaid on retinal images to provide clear explainability. All outputs will be neatly organized into labeled folders for easy presentation. The final notebook/report will summarize data splits, architecture, training details, and key insights in a concise academic style. My focus will be on clarity, reproducibility, and explainability so the project is impactful beyond raw performance. This combination of technical rigor and presentation‑readiness will ensure your project stands out in any academic or professional setting.

@ApieTech

APIE Tech has strong experience in medical AI/ML projects using PyTorch and deep learning for image classification and detection tasks. This scope is well within our capability. Our approach: multimodal architecture combining a CNN/ViT backbone for fundus image features with a lightweight MLP for clinical metadata, fused at a late-fusion layer. We will handle OLIVES dataset preprocessing, class imbalance via weighted sampling or focal loss, and produce all required metrics: Accuracy, Precision, F1, AUC, Cohen Kappa. Explainability outputs: confusion matrix, ROC/PR curves, calibration plots, Dice, IoU, feature importance, attention maps, and Grad-CAM overlays on retinal images. Deliverables exactly as specified: end-to-end reproducible Jupyter notebook, saved model weights + inference script, labelled visual outputs folder, and a 2-page clinical interpretability write-up. Clean, well-commented code is our standard. Can start immediately and deliver in 10 days.

@ARAVIND046

Hi, Your project is exactly the kind of AI/ML work I enjoy—medical imaging, multimodal deep learning, and explainable AI with academic presentation quality. I have already worked extensively on training deep learning models for classification tasks, building reproducible pipelines, model evaluation, and generating explainability outputs, so this project aligns well with my experience. I’m curious about one thing before we begin: are you targeting binary diabetic retinopathy detection or severity grading? That helps define the architecture and evaluation strategy properly. For this project, I can build a complete end-to-end reproducible pipeline using PyTorch (preferred) or TensorFlow. This includes OLIVES dataset preparation, preprocessing documentation, augmentation strategy, and class imbalance handling using suitable methods like weighted loss or sampling. If useful metadata is available, I can integrate it into a multimodal architecture alongside fundus image features. Deliverables will include full evaluation on a held-out test set with Accuracy, Precision, F1-score, ROC-AUC, and Cohen’s Kappa, along with academic-quality visual outputs such as confusion matrix, ROC curves, PR curves, calibration plots, Grad-CAM heatmaps, attention visualizations, feature importance analysis, and Dice/IoU if relevant. You’ll receive clean notebook/scripts, saved trained weights, inference code, organized visual outputs, and a concise academic explanation.

@rezassarkhosh

Hello, I am an MSc researcher in Machine Learning for Healthcare at the University of Padova with research experience in medical AI, computer vision, explainable AI, and trustworthy machine learning. I also have published/accepted research papers in AI and federated learning. Your project fits very well with my background, especially the combination of deep learning, medical imaging, and explainability. I can deliver: • Complete OLIVES preprocessing pipeline and class imbalance handling • Clean multimodal model in PyTorch/TensorFlow • Full evaluation metrics (Accuracy, F1, AUC, Cohen Kappa, Dice, IoU, etc.) • Explainability outputs including Grad-CAM, attention maps, ROC/PR curves, calibration plots, and confusion matrices • Reproducible notebook/scripts, saved weights, inference pipeline, and concise academic-style documentation I focus on clean research-grade implementation, reproducibility, and presentation-quality outputs suitable for academic demonstrations. Best regards, Reza Sarkhosh MSc Researcher — University of Padova